Hello everyone,

I have a panel data set which comprises of 25 countries and 2000 banks. In order to test if the effect of my independent variables (x1, x2, x3,x4) on y (dependent variable) changes as per the level of development of countries, I run my base regression separately for developed countries and emerging countries, which is similar to;

Method 1

xtreg y x1 x2 x3 x4 i.year if dev=="Developed", fe vce(cluster firm_id) (1)

and

xtreg y x1 x2 x3 x4 i.year if dev=="Emerging", fe vce(cluster firm_id) (2)

Once those regressions are performed, the coefficient of x2 on developed countries is positive statistically significant at 1% but the corresponding coefficient of x2 on emerging countries is positive but not statistically significant, according to p values. Hence, I interpreted the results as, the effect of x2 on y is only significant in developed countries.

However, when I run a joint model with interaction terms as indicated below, the interaction term between x2 and the dummy group variable (d.dummy = 1 if countries are developed and 0 if they are emerging), that interaction term is not significant.

Method 2

xtreg x1 x2 x3 x4 i.year (c.x1 c.x2 c.x3 c.x4 i.year)#d.dummy, fe vce(cluster firm_id)

d_dummy#c.x2 p value is 0.914.

All other statistics are similar; in terms of the number of observations and I get same coefficients for all independent variables from both methods.

My questions are;
(01) is it incorrect to interpret that the effect of x2 on y is significant only in developed countries as I concluded by method 1, running regressions for two sub samples?
(02) Why are the results different between the two methods?

Thank You.